Training models

Machine Learning is so aptly named because it uses various forms of training to analyze data or state and provide that trained response. These methods are worth mentioning and we will focus on one particular method of learning that is currently showing good success. Before we get to that though, for later chapters, let's breakdown the three types of training we frequently see in ML:

• Unsupervised Training: This method of training examines a dataset on its own and performs a classification. The classification may be based on certain metrics and can be discovered by the training itself. Most people used to think that all AI or ML worked this way, but of course, it does not:
  • ESRI, which is a major mapping provider of GIS software and data provides a demographic dataset called Tapestry. This dataset is derived from a combination of US census data and other resources. It is processed through an ML algorithm that classifies the data into 68 consumer segments using Unsupervised Training. The Tapestry data is not free but can be invaluable for anyone building ML for a consumer or retail application.
• Supervised Training: This is the typical training method most data science ML methods use to perform prediction or classification. It is a type of training that requires input and output data be labelled. As such, it requires a set of training data in order to build a model. Oftentimes, depending on the particular ML technique, it can require vast amounts of data:
  • Google Inception is an image classification ML model that is freely available. It has been trained by millions of images into various trained classifications. The Inception model is small enough to fit on a mobile device in order to provide real-time image classification.
• Reinforcement Learning: This is based on control theory and provides a method of learning without any initial state or model of the environment. This is a powerful concept because it eliminates the need to model the environment or undertake the tedious data labeling often required by Supervised Training. Instead, agents are modeled in the environment and receive rewards based on their actions. Of course, that also means that this advanced method of training is not without its pitfalls and frustrations. We will start learning the details of RL in Chapter 2, The Bandit and Reinforcement Learning:
  • DeepMind built the bot that was able to play classic Atari 2600 games better than a human.

• Imitation Learning: This is a technique where agents are trained by watching a demonstration of the desired actions and then imitating them. This is a powerful technique and has plenty of applications. We will explore this type of training in Chapter 4, Going Deeper with Deep Learning.
• Curriculum Learning: This is an advanced form of learning that works by breaking down a problem into levels of complexity, which allows the agent or ML to overcome each level of complexity before moving on to more advanced activities. For example, an agent waiter may first need to learn to balance a tray, then the tray with a plate of food, then walking with the tray and food, and finally delivering the food to a table. We will explore this form of training in Chapter 5, Playing the Game.
• Deep Learning: This uses various forms of internal training mechanisms to train a multi-layer neural network. We will spend more time on neural networks and Deep Learning in Chapter 3, Deep Reinforcement Learning with Python.

You may have already noticed the interchange of terms ML and agent use to denote the thing that is learning. It is helpful to think of things in these terms for now. Later in this chapter, we will start to distinguish the differences between an agent and their brain or ML. For now, though, let's get back to some basics and explore a simple ML example in the next section.